{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GLM: Linear regression\n",
    "\n",
    "This tutorial is adapted from a [blog post by Thomas Wiecki called \"The Inference Button: Bayesian GLMs made easy with PyMC3\"](http://twiecki.github.io/blog/2013/08/12/bayesian-glms-1/).\n",
    "\n",
    "This tutorial appeared as a post in a small series on Bayesian GLMs on my blog:\n",
    "\n",
    "  1. [The Inference Button: Bayesian GLMs made easy with PyMC3](http://twiecki.github.com/blog/2013/08/12/bayesian-glms-1/)\n",
    "  2. [This world is far from Normal(ly distributed): Robust Regression in PyMC3](http://twiecki.github.io/blog/2013/08/27/bayesian-glms-2/)\n",
    "  3. [The Best Of Both Worlds: Hierarchical Linear Regression in PyMC3](http://twiecki.github.io/blog/2014/03/17/bayesian-glms-3/)\n",
    "  \n",
    "In this blog post I will talk about:\n",
    "\n",
    "  - How the Bayesian Revolution in many scientific disciplines is hindered by poor usability of current Probabilistic Programming languages.\n",
    "  - A gentle introduction to Bayesian linear regression and how it differs from the frequentist approach.\n",
    "  - A preview of [PyMC3](https://github.com/pymc-devs/pymc/tree/pymc3) (currently in alpha) and its new GLM submodule I wrote to allow creation and estimation of Bayesian GLMs as easy as frequentist GLMs in R.\n",
    "\n",
    "Ready? Lets get started!\n",
    "\n",
    "There is a huge paradigm shift underway in many scientific disciplines: The Bayesian Revolution. \n",
    "\n",
    "While the theoretical benefits of Bayesian over Frequentist stats have been discussed at length elsewhere (see *Further Reading* below), there is a major obstacle that hinders wider adoption -- *usability* (this is one of the reasons DARPA wrote out a huge grant to [improve Probabilistic Programming](http://www.darpa.mil/program/probabilistic-programming-for-advancing-machine-Learning)). \n",
    "\n",
    "This is mildly ironic because the beauty of Bayesian statistics is their generality. Frequentist stats have a bazillion different tests for every different scenario. In Bayesian land you define your model exactly as you think is appropriate and hit the *Inference Button(TM)* (i.e. running the magical MCMC sampling algorithm).\n",
    "\n",
    "Yet when I ask my colleagues why they use frequentist stats (even though they would like to use Bayesian stats) the answer is that software packages like SPSS or R make it very easy to run all those individuals tests with a single command (and more often then not, they don't know the exact model and inference method being used).\n",
    "\n",
    "While there are great Bayesian software packages like [JAGS](http://mcmc-jags.sourceforge.net/), [BUGS](http://www.mrc-bsu.cam.ac.uk/bugs/), [Stan](http://mc-stan.org/) and [PyMC](http://pymc-devs.github.io/pymc/), they are written for Bayesians statisticians who know very well what model they want to build. \n",
    "\n",
    "Unfortunately, [\"the vast majority of statistical analysis is not performed by statisticians\"](http://simplystatistics.org/2013/06/14/the-vast-majority-of-statistical-analysis-is-not-performed-by-statisticians/) -- so what we really need are tools for *scientists* and not for statisticians.\n",
    "\n",
    "In the interest of putting my code where my mouth is I wrote a submodule for the upcoming [PyMC3](https://github.com/pymc-devs/pymc/tree/pymc3) that makes construction of Bayesian Generalized Linear Models (GLMs) as easy as Frequentist ones in R.\n",
    "\n",
    "## Linear Regression\n",
    "\n",
    "While future blog posts will explore more complex models, I will start here with the simplest GLM -- linear regression.\n",
    "In general, frequentists think about Linear Regression as follows:\n",
    "\n",
    "$$ Y = X\\beta + \\epsilon $$\n",
    "\n",
    "where $Y$ is the output we want to predict (or *dependent* variable), $X$ is our predictor (or *independent* variable), and $\\beta$ are the coefficients (or parameters) of the model we want to estimate. $\\epsilon$ is an error term which is assumed to be normally distributed. \n",
    "\n",
    "We can then use Ordinary Least Squares or Maximum Likelihood to find the best fitting $\\beta$.\n",
    "\n",
    "## Probabilistic Reformulation\n",
    "\n",
    "\n",
    "Bayesians take a probabilistic view of the world and express this model in terms of probability distributions. Our above linear regression can be rewritten to yield:\n",
    "\n",
    "$$ Y \\sim \\mathcal{N}(X \\beta, \\sigma^2) $$\n",
    "\n",
    "In words, we view $Y$ as a random variable (or random vector) of which each element (data point) is distributed according to a Normal distribution. The mean of this normal distribution is provided by our linear predictor with variance $\\sigma^2$.\n",
    "\n",
    "While this is essentially the same model, there are two critical advantages of Bayesian estimation:\n",
    "\n",
    " - Priors: We can quantify any prior knowledge we might have by placing priors on the paramters. For example, if we think that $\\sigma$ is likely to be small we would choose a prior with more probability mass on low values.\n",
    " - Quantifying uncertainty: We do not get a single estimate of $\\beta$ as above but instead a complete posterior distribution about how likely different values of $\\beta$ are. For example, with few data points our uncertainty in $\\beta$ will be very high and we'd be getting very wide posteriors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bayesian GLMs in PyMC3\n",
    "\n",
    "With the new GLM module in PyMC3 it is very easy to build this and much more complex models.\n",
    "\n",
    "First, lets import the required modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from pymc3 import  *\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create some toy data to play around with and scatter-plot it. \n",
    "\n",
    "Essentially we are creating a regression line defined by intercept and slope and add data points by sampling from a Normal with the mean set to the regression line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "size = 200\n",
    "true_intercept = 1\n",
    "true_slope = 2\n",
    "\n",
    "x = np.linspace(0, 1, size)\n",
    "# y = a + b*x\n",
    "true_regression_line = true_intercept + true_slope * x\n",
    "# add noise\n",
    "y = true_regression_line + np.random.normal(scale=.5, size=size)\n",
    "\n",
    "data = dict(x=x, y=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7, 7))\n",
    "ax = fig.add_subplot(111, xlabel='x', ylabel='y', title='Generated data and underlying model')\n",
    "ax.plot(x, y, 'x', label='sampled data')\n",
    "ax.plot(x, true_regression_line, label='true regression line', lw=2.)\n",
    "plt.legend(loc=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimating the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets fit a Bayesian linear regression model to this data. As you can see, model specifications in `PyMC3` are wrapped in a `with` statement. \n",
    "\n",
    "Here we use the awesome new [NUTS sampler](http://arxiv.org/abs/1111.4246) (our Inference Button) to draw 2000 posterior samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [x, Intercept, sigma]\n",
      "Sampling 2 chains: 100%|██████████| 7000/7000 [00:05<00:00, 1192.86draws/s]\n",
      "The acceptance probability does not match the target. It is 0.887029400585909, but should be close to 0.8. Try to increase the number of tuning steps.\n"
     ]
    }
   ],
   "source": [
    "with Model() as model: # model specifications in PyMC3 are wrapped in a with-statement\n",
    "    # Define priors\n",
    "    sigma = HalfCauchy('sigma', beta=10, testval=1.)\n",
    "    intercept = Normal('Intercept', 0, sigma=20)\n",
    "    x_coeff = Normal('x', 0, sigma=20)\n",
    "    \n",
    "    # Define likelihood\n",
    "    likelihood = Normal('y', mu=intercept + x_coeff * x, \n",
    "                        sigma=sigma, observed=y)\n",
    "    \n",
    "    # Inference!\n",
    "    trace = sample(3000, cores=2) # draw 3000 posterior samples using NUTS sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This should be fairly readable for people who know probabilistic programming. However, would my non-statistican friend know what all this does? Moreover, recall that this is an extremely simple model that would be one line in R. Having multiple, potentially transformed regressors, interaction terms or link-functions would also make this much more complex and error prone. \n",
    "\n",
    "The new `glm()` function instead takes a [Patsy](http://patsy.readthedocs.org/en/latest/quickstart.html) linear model specifier from which it creates a design matrix. `glm()` then adds random variables for each of the coefficients and an appopriate likelihood to the model. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sd, x, Intercept]\n",
      "Sampling 2 chains: 100%|██████████| 7000/7000 [00:08<00:00, 791.41draws/s]\n",
      "The acceptance probability does not match the target. It is 0.8810283834417805, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The acceptance probability does not match the target. It is 0.8886715521858014, but should be close to 0.8. Try to increase the number of tuning steps.\n"
     ]
    }
   ],
   "source": [
    "with Model() as model:\n",
    "    # specify glm and pass in data. The resulting linear model, its likelihood and \n",
    "    # and all its parameters are automatically added to our model.\n",
    "    glm.GLM.from_formula('y ~ x', data)\n",
    "    trace = sample(3000, cores=2) # draw 3000 posterior samples using NUTS sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much shorter, but this code does the exact same thing as the above model specification (you can change priors and everything else too if we wanted). `glm()` parses the `Patsy` model string, adds random variables for each regressor (`Intercept` and slope `x` in this case), adds a likelihood (by default, a Normal is chosen), and all other variables (`sigma`). Finally, `glm()` then initializes the parameters to a good starting point by estimating a frequentist linear model using [statsmodels](http://statsmodels.sourceforge.net/devel/).\n",
    "\n",
    "If you are not familiar with R's syntax, `'y ~ x'` specifies that we have an output variable `y` that we want to estimate as a linear function of `x`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bayesian inference does not give us only one best fitting line (as maximum likelihood does) but rather a whole posterior distribution of likely parameters. Lets plot the posterior distribution of our parameters and the individual samples we drew."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/junpenglao/anaconda3/lib/python3.6/site-packages/matplotlib/figure.py:2144: UserWarning: This figure was using constrained_layout==True, but that is incompatible with subplots_adjust and or tight_layout: setting constrained_layout==False. \n",
      "  warnings.warn(\"This figure was using constrained_layout==True, \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 504x504 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 7))\n",
    "traceplot(trace[100:])\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The left side shows our marginal posterior -- for each parameter value on the x-axis we get a probability on the y-axis that tells us how likely that parameter value is.\n",
    "\n",
    "There are a couple of things to see here. The first is that our sampling chains for the individual parameters (left side) seem well converged and stationary (there are no large drifts or other odd patterns).\n",
    "\n",
    "Secondly, the maximum posterior estimate of each variable (the peak in the left side distributions) is very close to the true parameters used to generate the data (`x` is the regression coefficient and `sigma` is the standard deviation of our normal)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the GLM we thus do not only have one best fitting regression line, but many. A posterior predictive plot takes multiple samples from the posterior (intercepts and slopes) and plots a regression line for each of them. Here we are using the `plot_posterior_predictive_glm()` convenience function for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 7))\n",
    "plt.plot(x, y, 'x', label='data')\n",
    "plot_posterior_predictive_glm(trace, samples=100, \n",
    "                              label='posterior predictive regression lines')\n",
    "plt.plot(x, true_regression_line, label='true regression line', lw=3., c='y')\n",
    "\n",
    "plt.title('Posterior predictive regression lines')\n",
    "plt.legend(loc=0)\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, our estimated regression lines are very similar to the true regression line. But since we only have limited data we have *uncertainty* in our estimates, here expressed by the variability of the lines."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - Usability is currently a huge hurdle for wider adoption of Bayesian statistics.\n",
    " - `PyMC3` allows GLM specification with convenient syntax borrowed from R.\n",
    " - Posterior predictive plots allow us to evaluate fit and our uncertainty in it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further reading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the first post of a small series on Bayesian GLMs I am preparing. Next week I will describe how the Student T distribution can be used to perform robust linear regression.\n",
    "\n",
    "Then there are also other good resources on Bayesian statistics:\n",
    "\n",
    "  - The excellent book [Doing Bayesian Data Analysis by John Kruschke](http://www.indiana.edu/~kruschke/DoingBayesianDataAnalysis/).\n",
    "  - [Andrew Gelman's blog](http://andrewgelman.com/)\n",
    "  - [Baeu Cronins blog post on Probabilistic Programming](https://plus.google.com/u/0/107971134877020469960/posts/KpeRdJKR6Z1)\n",
    "  \n",
    "Author: Thomas Wiecki"
   ]
  }
 ],
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